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Machine Learning with
PyCaret
Moez Ali
Creator of PyCaret
Agenda
§ Introduction
§ Machine Learning Life Cycle
§ PyCaret
§ Demo
§ Q&A
About me
https://www.linkedin.com/in/profile-moez/
https://twitter.com/moezpycaretorg1
https://medium.com/@moez_62905/
moe...
Important Links
• Official: https://www.pycaret.org
• GitHub: https://www.github.com/pycaret/pycaret
• LinkedIn: https://w...
Machine Learning Life Cycle
Business
Problem
Data
Sourcing
& ETL
Exploratory
Data Analysis
(EDA)
Data
Prep.
Model
Training...
Data Prep, Model Training and Selection
Data
Train Test
Split
Test
Train
Missing Values
Imputation
Feature
Scaling
Encodin...
Challenges of Machine Learning Lifecycle
● Machine Learning is an iterative process. It is very time consuming.
● Right to...
What is PyCaret?
PyCaret is an open source, low-code machine learning library and end-to-end model
management used to auto...
PyCaret Features
Model
Selection
Analysis &
Interpretability
Experiment
Logging
Data
Preparation
Model
Training
Hyperparam...
Machine Learning use-case supported:
Classification Regression
Clustering
Anomaly
Detection
Association
Rule
Mining
Natura...
Impact of PyCaret
0
20
40
60
80
100
120
140
160
180
Data Preparation Model Training Model Selection Model Evaluation
Cumul...
Statistics
Downloads
500,000+
Git Stars
3,000+
Contributors
46
Commits
1,700+
Contributors
PyCaret on GPU and on distributed cluster
Training on GPU Scalable Hyperparameter Tuning
PyCaret Integrations
Demo
qDemo 1 – Regression Problem on Insurance data
qDemo 2 – Time Series Forecasting using Regression
qDemo 3 – Multiple ...
Feedback
Your feedback is important to us.
Don’t forget to rate and review the sessions.
APPENDIX
Resources
Resources (cont.)
Resources (cont.)
Resources (cont.)
Resources (cont.)
Resources (cont.)
Resources (cont.)
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Machine Learning with PyCaret

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PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.

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Machine Learning with PyCaret

  1. 1. Machine Learning with PyCaret Moez Ali Creator of PyCaret
  2. 2. Agenda § Introduction § Machine Learning Life Cycle § PyCaret § Demo § Q&A
  3. 3. About me https://www.linkedin.com/in/profile-moez/ https://twitter.com/moezpycaretorg1 https://medium.com/@moez_62905/ moez@pycaret.org • Background: Finance + Economics + Computer Science + Data Science • Industry: Healthcare + Education + Consulting + Fintech • Work: Asia + Middle East + East Africa + North America • Open-Source: PyCaret
  4. 4. Important Links • Official: https://www.pycaret.org • GitHub: https://www.github.com/pycaret/pycaret • LinkedIn: https://www.linkedin.com/company/pycaret • YouTube: https://www.youtube.com/channel/UCxA1YTYJ9BEeo50lxyI_B3g • Medium: https://medium.com/@moez_62905/ • Slack: https://join.slack.com/t/pycaret/shared_invite/zt-nm02k73a-PSDo5lwmQ6evlFCPRxsKKA • Demo: https://www.github.com/pycaret/pycaret-demo-dataai2021
  5. 5. Machine Learning Life Cycle Business Problem Data Sourcing & ETL Exploratory Data Analysis (EDA) Data Prep. Model Training & Selection Deployment & Monitoring Data Prep. Model Training & Selection
  6. 6. Data Prep, Model Training and Selection Data Train Test Split Test Train Missing Values Imputation Feature Scaling Encodings Feature Engineering Cross Validation Environment Model Training Model Tuning Model Ensemble Model Selection Finalize Pipeline Deploy Monitor Optional
  7. 7. Challenges of Machine Learning Lifecycle ● Machine Learning is an iterative process. It is very time consuming. ● Right tooling in the hands of right people is very important. ● Creating a seamless pipeline is hard. Managing it in production is harder. ● Focus on end-goal and solving business problems can take a backseat within small teams with increasing technical debt. ● Scalability is not just desirable, but it is very much needed.
  8. 8. What is PyCaret? PyCaret is an open source, low-code machine learning library and end-to-end model management used to automate machine learning workflows. It is commonly used for rapid prototyping and deployment of ML pipelines. EASY TO USE PRODUCTIVITY TOOL BUSINESS READY
  9. 9. PyCaret Features Model Selection Analysis & Interpretability Experiment Logging Data Preparation Model Training Hyperparameter Tuning
  10. 10. Machine Learning use-case supported: Classification Regression Clustering Anomaly Detection Association Rule Mining Natural Language Processing SUPERVISED UNSUPERVISED Time Series
  11. 11. Impact of PyCaret 0 20 40 60 80 100 120 140 160 180 Data Preparation Model Training Model Selection Model Evaluation Cumulative Lines of Code Comparison scikit-learn PyCaret Cumulative lines of code Machine Learning Workflow Level
  12. 12. Statistics Downloads 500,000+ Git Stars 3,000+ Contributors 46 Commits 1,700+
  13. 13. Contributors
  14. 14. PyCaret on GPU and on distributed cluster Training on GPU Scalable Hyperparameter Tuning
  15. 15. PyCaret Integrations
  16. 16. Demo qDemo 1 – Regression Problem on Insurance data qDemo 2 – Time Series Forecasting using Regression qDemo 3 – Multiple Time Series Forecasting qDemo 4 – Model serving through MLflow API All Notebooks are available here: https://www.github.com/pycaret/pycaret-demo-dataai2021
  17. 17. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.
  18. 18. APPENDIX
  19. 19. Resources
  20. 20. Resources (cont.)
  21. 21. Resources (cont.)
  22. 22. Resources (cont.)
  23. 23. Resources (cont.)
  24. 24. Resources (cont.)
  25. 25. Resources (cont.)

PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.

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